Summary
Nic Fishman is a PhD student in Statistics at Harvard with 11 years of applied research and engineering experience at the intersection of machine learning, causal inference, and the science of science. He builds end-to-end systems—from MySQL-backed polling infrastructure and automated analysis pipelines used in political campaigns to probabilistic graphical models and diffusion models for protein design—while publishing methodological work on nonparametric Bayesian and variational inference. His projects span huge observational datasets (location traces, grant and abstract corpora) and tightly controlled experimental settings (adaptive conjoint designs and sequence optimization), unified by careful treatment of nonconvex optimization and uncertainty quantification. Nic combines academic rigor from institutions including Stanford, Oxford, MIT and Harvard with practical impact at organizations like Data for Progress and Google, and brings a rare fluency in both policy-focused data work and generative biology.
11 years of coding experience
3 years of employment as a software developer
The George Washington University
High School Diploma, High School Diploma at Woodrow Wilson High School
Masters by Research Statistics, Masters by Research Statistics at University of Oxford
Doctor of Philosophy - PhD Statistics, Doctor of Philosophy - PhD Statistics at Harvard University
Bachelor's degree Computer Science and Sociology with Honors, Bachelor's degree Computer Science and Sociology with Honors at Stanford University
English, Spanish